A new hybrid model, the wavelet–bootstrap–ANN (WBANN), for daily discharge forecasting is proposed in this study. The study explores the potential of wavelet and bootstrapping techniques to develop an accurate and reliable ANN model. The performance of the WBANN model is also compared with three more models: traditional ANN, wavelet-based ANN (WANN) and bootstrap-based ANN (BANN). Input vectors are decomposed into discrete wavelet components (DWCs) using discrete wavelet transformation (DWT) and then appropriate DWCs sub-series are used as inputs to the ANN model to develop the WANN model. The BANN model is an ensemble of several ANNs built using bootstrap resamples of raw datasets, whereas the WBANN model is an ensemble of several ANNs built using bootstrap resamples of DWCs instead of raw datasets. The results showed that the hybrid models WBANN and WANN produced significantly better results than the traditional ANN and BANN, whereas the BANN model is found to be more reliable and consistent. The WBANN and WANN models simulated the peak discharges better than the ANN and BANN models, whereas the overall performance of WBANN, which uses the capabilities of both bootstrap and wavelet techniques, is found to be more accurate and reliable than the remaining three models.
Keywords: bootstrap, discharge, forecasting, resampling, wavelet